Areas of Research Interest

Field of cognitive architectures are large and quickly growing. We keep our research focus on four main directions which have potential for disruption in artificial intelligence and machine learning. These fields are tightly interconnected and our research projects usually relate to more than one area.

AI & Complexity science

Artificially-intelligent agents are dynamic systems exhibiting such properties as chaos, strange attractors, fractal dimensions, different forms of entropy etc. We focus on finding how methods of non-linear science, chaotic time series analysis, fractal calculus, graphs, catastrophes theory etc may be applied to enhance machine learning algorithms.

Communications in multi-agent systems

These includes collective intelligence and behavior in multi-agent intelligent systems; research on the efficient ways of trusted information exchange between artificially intelligent agents; swarming of drones and robots as an application of former and later. In this direction we do a lot of research on usage of blockchain-like protocols as en efficient tool for such tasks.

New BICA architectures

We use cross-disciplinary approach to bring advances in neuroscience, psychology and psychiatry into mathematical and algorithmic models that can be used for creating new efficient cognitive architectures. Rather then taking large-scale brain properties as a basis for cognitive architectures (like deep neural networks), we primarely focus on neurophysilogical properties of synapses and neurons (neuroplasticity, receptor dynamics), genetics, emotions, neuroendocrine factors (like those involved in stress) underling human intelligence, its formation and learning.

Embedded AI and brain-computer interfaces

Our labs performs some ground research into how brain and computer processes can be connected and how they can exchange information. These includes de-ciphering different types of brain activity (EEG, neuronal spikes) and their correlates (mimics etc) with special machine-learning algorithms; finding ways to transfer information into brain or affect conscious and subconscious states, emotions with non-invasive transcranial methods; finding approaches for the development of embedded systems , chips that will process AI efficiently.

Find out more on our current and incubating-stage research projects and groups